Cannet A. auteur aut IRD Simon-Chane C. auteur aut IRD Akhoundi M. auteur aut IRD Histace A. auteur aut IRD Romain O. auteur aut IRD Souchaud M. auteur aut IRD Jacob P. auteur aut IRD Sereno D. auteur aut IRD Mouline Karine auteur aut IRD Barnabé Christian auteur aut IRD Lardeux Frédéric auteur aut IRD Boussès Philippe auteur aut IRD Sereno Denis auteur aut IRD text journalArticle eng text/pdf reformatted digital access We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (>65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases. specialized 052 020 13 1 13895 [13 ] 2023 2045-2322 https://www.documentation.ird.fr/hor/fdi:010088569 10.1038/s41598-023-41114-4 2045-2322 [F B010088569] https://www.documentation.ird.fr/hor/fdi:010088569 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-11/010088569.pdf IRD - Base Horizon / Pleins textes 2023-11-10 2025-02-24 fdi:010088569 fre